The aims of this project are to 1) identify patient and trial-design factors that contribute to variation in placebo response in clinical trials of new medications for the treatment of patients with schizophrenia, and 2) build predictive models of placebo response which will enable investigators to predict and control the influence of placebo response in specific trial protocols and patient populations. The ability to predict and control variation in placebo response within and across clinical trials would allow the number of patients exposed to placebo in future trials of antipsychotic medications to be minimized and would accelerate the determination of the efficacy of new drugs. Individual clinical trials and traditional meta-analyses of trial summary data can provide only limited information about moderators of placebo response. We propose to model the effects of potential moderators by applying modern hierarchical statistical methods to individual patient data from clinical trials of new medications in patients meeting DSM-III/IV criteria for schizophrenia or schizoaffective disorder. Hierarchical models can incorporate variables measured at the level of patients as well as at the level of trials (e.g., differences in protocols), allowing the total placebo response variation to be validly decomposed and attributed to factors operating at each level. We hypothesize that duration of placebo treatment, duration of illness, and baseline symptom severity will be among the factors that determine response to placebo.